Additionally, there are many previous supervised topic models that directly incorporate the supervision information into the unsupervised versions, including both works on single-label learning [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} and multi-label classification [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}. These models have empirically achieved very competitive classification performance, however, they require labeled documents as inputs. In contrast, our Wsptm trains a classifier only using the much cheaper seed words, hence saving many human efforts on collecting labeled training corpora.
